PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
DISC: data-intensive similarity measure for categorical data
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Coupled nominal similarity in unsupervised learning
Proceedings of the 20th ACM international conference on Information and knowledge management
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Similarity between complex data objects is one of the central notions in data mining. We propose certain similarity (or distance) measures between various components of a 0/1 relation. We define measures between attributes, between rows, and between subrelations of the database. They find important applications in clustering, classification, and several other data mining processes. Our measures are based on the contexts of individual components. For example, two products (i.e., attributes) are deemed similar if their respective sets of customers (i.e., subrelations) are similar. This reveals more subtle relationships between components, something that is usually missing in simpler measures. Our problem of finding distance measures can be formulated as a system of nonlinear equations. We present an iterative algorithm which, when seeded with random initial values, converges quickly to stable distances in practice (typically requiring less than five iterations). The algorithm requires only one database scan. Results on artificial and real data show that our method is efficient, and produces results with intuitive appeal.